Summary

Obtaining physiological/behavioral data from human subjects in natural environments is essential to the effectiveness and accuracy of social and behavioral research, such as ecologically valid studies of psychosocial stress, addictive cravings, and environmental toxicants. Many research questions of critical importance to human society have remained unanswered for lack of such data, due primarily to the fact that they (e.g. physiological response associated with panic) typically cannot be reli- ably simulated in clinical and self-report settings. While several body area wireless sensor network (BAWSN) systems exist today for physiological data collection, their use however, has been re- stricted to controlled settings (laboratories, driving/flying scenarios, etc.); significant noise, motion artifacts, and existence of other uncontrollable confounding factors are the often cited reasons for not using physiological measurements from natural environments. In order to provide scientifically valid data for exposure biology studies from natural environments, a BAWSN system must meet several unique requirements which are not met by current BAWSN systems: (1) Stringent data quality without sensing redundancy, (2) Personalization to account for wide between person dif- ferences in physiological measurements, (3) Real-time inferencing to allow for subject confirmation and timely intervention.

Intellectual Merit: We propose to develop a general purpose framework called FieldStream that will make it possible for BAWSN systems to provide long term unattended collection of objective, continuous, and reliable physiological/behavioral data from natural environments that can be used for conducting population based scientific studies. FieldStream will use context and model based prudent sampling to optimize when and how frequently to sample which sensors. A base model will be learned for each sensor from collected data across the population, and then it will be personalized to each subject, and its various states (e.g., placement, context, etc.) in real-time. This model will then be used to optimize sampling and to automatically validate the collected samples. Although collected samples can not be wirelessly transmitted continuously in real-time, recent advances in flash based storage make it possible to store large amounts of collected samples locally. Real-time inferencing and real-time queries on locally stored data, will, however, be supported to enable triggering of Ecological Momentary Assessment (EMA) inputs from the participants and to trigger real-time behavioral interventions. To address the research challenges that arise in designing these system components, we have assembled a multi-disciplinary team with expertise spanning various relevant CS and EE disciplines.

The research will be validated by real-life testing of FieldStream in at least two federally sponsored real-life projects — NIH sponsored AutoSense at Memphis and NSf sponsored Urban Sensing at UCLA, to help validate the assumptions, establish the feasibility of developed solutions, and uncover new requirements for FieldStream.

Broader Impact: The social and behavioral science community is extremely excited about the tremendous advancement possible with the field deployable sensing systems, but is now skeptical of its feasibility given the above mentioned daunting computing and networking challenges that must be overcome. If these issues are not addressed in a timely fashion, these real-life deployments will not live up to their promise, the social and behavioral researchers will revert to the traditional tools, and the society will be deprived of the tremendous advancements possible to the quality of human life.